Dedicated GPU Servers

for AI & ML

Unlike cloud providers that offer instant provisioning from a shared pool, our GPU servers are procured specifically for you when you place an order. After payment confirmation (3-month advance), we source and configure your dedicated hardware. Setup takes 5-10 business days. This buy-when-ordered model is what allows us to offer bare metal pricing at 50-70% below hyperscaler rates.

Pricing has two components: a fixed monthly fee (covering hardware, rack space, bandwidth, IPs, and cooling) plus metered energy billed at actual consumption. There is no hourly billing. The minimum commitment is 3 months, paid in advance. After the initial term, billing continues monthly. All prices are listed in USD and converted at checkout based on your selected currency.

We offer a range of NVIDIA GPUs: T4 (16 GB, Turing), L4 (24 GB, Ada Lovelace), A30 (24 GB, Ampere), RTX A6000 (48 GB, Ampere), L40S (48 GB, Ada Lovelace), A100 (40 GB and 80 GB, Ampere), and H100 (80 GB, Hopper). Multi-GPU configurations are available including 2xA100, 4xA100, and 8xH100. Availability varies — check the pricing section or contact sales for current stock.

Every GPU server includes: a dedicated IPv4 and IPv6 address, IPMI/KVM remote console access, power and cooling, 24/7 NOC monitoring, and pre-installed NVIDIA drivers with CUDA toolkit and cuDNN. We can also pre-install PyTorch, TensorFlow, Jupyter, and Docker upon request. You get full root/admin access to the machine.

GPU VPS uses NVIDIA’s Multi-Instance GPU (MIG) technology to partition a single A100 into up to 7 isolated GPU instances. Each partition gets dedicated compute, memory, and cache — it’s hardware-level isolation, not time-slicing. MIG partitions are a lower-cost entry point for inference workloads or smaller training jobs that don’t need a full GPU.

GPU Colocation lets you bring your own GPU server hardware to our Bangalore datacenter. We provide the rack space, power (with higher kW reservations suited for GPU workloads), cooling, network connectivity, and 24/7 remote hands support. Energy is metered at the rack PDU. Ideal if you already own GPU hardware and want enterprise-grade infrastructure without building your own facility.

Our bare metal GPU servers are typically 50-70% cheaper than equivalent AWS/Azure/GCP GPU instances for sustained workloads. Unlike E2E Networks or hyperscalers that offer virtualized GPU instances, we provide dedicated bare metal with 100% GPU TFLOPS, no noisy neighbors, and full root access. Our Bangalore DC location also means lower latency for Indian workloads and data stays within Indian jurisdiction.

Yes. We support multi-GPU configurations (2x, 4x, 8x GPUs in a single server), custom RAM and storage options, and can source specific hardware on request. For enterprise requirements like InfiniBand interconnects, NVLink bridges, or cluster deployments, contact our sales team for a custom quote.

All GPU servers are hosted in our Bangalore datacenter under Indian jurisdiction. Your data never leaves India. This makes our infrastructure suitable for workloads subject to RBI data localization requirements, DPDP Act compliance, and other Indian data residency regulations.